Understanding the Shift: ChatGPT and its Effects on Traditional Online Purchase Determinants
الموضوعات :
الکلمات المفتاحية: Online purchase indicators, UTAUT, ChatGPT, Disruptions, Consumer behaviour, Artificial intelligence, Generative AI, Online consumer preferences, Technology adoption,
ملخص المقالة :
This research paper aims to comprehensively understand the shift brought about by ChatGPT, an advanced generative AI platform, and its effects on traditional online purchase determinants. Through the integration of a systematic literature review of 304 research articles on online purchase intentions, theories, and constructs, along with an examination of ChatGPT's disruptive capabilities, this study provides valuable insights into the transformative potential of AI in the marketing domain. The literature review process follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach, employing a top-down methodology. The findings reveal the dominance of TAM and UTAUT as theories in traditional online purchase determinants, while also identifying key determinants such as privacy concerns, rising purchase power parity, and online incentives. These determinants are further categorized based on geographical variations and standard of living, drawing insights from studies conducted across different regions. By exploring the disruptions caused by Chat GPT, this study bridges the gap in understanding the impact of AI technologies on traditional online purchase determinants. The insights gained from this research will assist marketers in effectively adapting their strategies to leverage Chat GPT's capabilities, taking into account factors such as personalization, privacy, data security, and ethical AI use. Moreover, there is a need to align existing studies such as TAM and UTAUT with recent constructs like Chat GPT, highlighting the importance of integrating new technological advancements into established models.
Ahmed, Z., Su, L., Rafique, K., Khan, S. Z., & Jamil, S. (2017). A study on the factors affecting consumer buying behavior towards online shopping in Pakistan. Journal of Asian Business Strategy, 7(2), 44.
• Al Karim, R. (2013). Customer Satisfaction in Online Shopping: a study into the reasons for motivations and inhibitions. IOSR Journal of Business and Management, 11(6), 13–20.
• Alam, S. S., & Yasin, N. M. (2010). What factors influence online brand trust: evidence from online tickets buyers in Malaysia. Journal of theoretical and applied electronic commerce research, 5(3), 78-89.
• Alba, J. W., & Hutchinson, J. W. (1987). Dimensions of consumer expertise. Journal of consumer research, 13(4), 411-454.
• Arnott, D. C., Wilson, D., Mukherjee, A., & Nath, P. (2007). Role of electronic trust in online retailing. European journal of marketing.
• Bagla, R. K., & Khan, J. (2017). Customers' expectations and satisfaction with online food ordering portals. Prabandhan: Indian Journal of Management, 10(11), 31-44.
• Bashir, R., Mehboob, I., & Bhatti, W. K. (2015). Effects of online shopping trends on consumer-buying Behavior: an empirical study of Pakistan. Journal of Management and Research, 2(2), 1–24.
• Bellegarda, J. R. (2004). Statistical language model adaptation: review and perspectives. Speech communication, 42(1), 93-108.
• Bellman, S., Lohse, G. L., & Johnson, E. J. (1999). Predictors of online buying behavior. Communications of the ACM, 42(12), 32–38.
• Brown, S. A., & Venkatesh, V. (2005). Model of adoption of technology in households: A baseline model test and extension incorporating household life cycle. MIS quarterly, 399-426.
• Chakraborty, T., & Balakrishnan, J. (2017). Exploratory tendencies in consumer behavior in online buying across gen X, gen Y, and baby boomers. International Journal of Value Chain Management, 8(2), 135–150.
• Cockburn, I. M., Henderson, R., & Stern, S. (2019). 4. The impact of artificial intelligence on innovation: An exploratory analysis. In The economics of artificial intelligence (pp. 115-148). University of Chicago Press.
• Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 189-211.
• Dharmesti, M. D. D., & Nugroho, S. S. (2013). The antecedents of online customer satisfaction and customer loyalty. Journal of Business and Retail Management Research, 7(2).
• Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results (Doctoral dissertation, Massachusetts Institute of Technology).
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
• Dash, M. S., & Krishna, C. V. (2011). Next-generation retailing in India: An empirical study using factor analysis. International Review of management and marketing, 1(2), 25-29.
• Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., ... & Wright, R. (2023). “So what if ChatGPT
P. Ramesh Bari & et al.
18
wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642.
• Doshi, R. H., Bajaj, S. S., & Krumholz, H. M. (2023). ChatGPT: temptations of progress. The American Journal of Bioethics, 23(4), 6-8.
• D'Alessandro, S., Girardi, A., & Tiangsoongnern, L. (2012). Perceived risk and trust as antecedents of online purchasing behavior in the USA gemstone industry. Asia Pacific Journal of Marketing and Logistics, 24(3), 433-460.
• Fishbein, M., & Ajzen, I. (1975). Intention and Behaviour: An introduction to theory and research.
• George, J. F. (2004). The theory of planned behavior and Internet purchasing. Internet research.
• Gupta, A., Dogra, N., & George, B. (2018). What determines tourist adoption of smartphone apps? An analysis based on the UTAUT-2 framework. Journal of Hospitality and Tourism Technology, 9(1), 50-64.
• Gupta, S. L., Mittal, R., & Nayyar, R. (2011). Retailing and E-tailing. International Book House.
https://economictimes.indiatimes.com/industry/services/retail/indian-e-tailing-industry-may-touch-usd-28-billion-by-fy2020/articleshow/54091296.cms
• Jena, L. K., & Goyal, S. (2022). Emotional intelligence and employee innovation: Sequential mediating effect of person-group fit and adaptive performance. European Review of Applied Psychology, 72(1), 100729.
• Hall, D. T., & Mansfield, R. (1975). Relationships of age and seniority with
career variables of engineers and scientists. Journal of Applied Psychology, 60(2), 201.
• Khan, S. A., Liang, Y., & Shahzad, S. (2015). An empirical study of perceived factors affecting customer satisfaction to re-purchase intention in online stores in China. Journal of Service Science and Management, 8(03), 291.
• Kushwaha, A. K., Kar, A. K., & Dwivedi, Y. K. (2021). Applications of big data in emerging management disciplines: A literature review using text mining. International Journal of Information Management Data Insights, 1(2), 100017.
• Liao, C., Palvia, P., & Lin, H. N. (2010). Stage antecedents of consumer online buying behavior. Electronic Markets, 20(1), 53–65.
• Lin, C., & Lekhawipat, W. (2014). Factors affecting online repurchase intention. Industrial Management & Data Systems, 114(4), 597–611.
• Lokman, A. S., & Ameedeen, M. A. (2018). Modern chatbot systems: A technical review. InProceedings of the future technologies conference (pp. 1012-1023).
• Mariani, M. M., Perez‐Vega, R., & Wirtz, J. (2022). AI in marketing, consumer research and psychology: A systematic literature review and research agenda. Psychology & Marketing, 39(4), 755-776.
• Melis, G., Dyer, C., & Blunsom, P. (2017). On the state of the art of evaluation in neural language models. arXiv preprint arXiv:1707.05589.
• Mohanty, A. K., & Panda, J. (2008). Retailing in India: Challenges and Opportunities. The Orissa Journal of Commerce, 29(2), 69-79.
• Panda, R., & Narayan Swar, B. (2013). Online Shopping: An Exploratory Study to Identify the Determinants of Shopper
Int. J. Manage. Bus., Vol 7, (4), 1-20, Autumn 2023
19
Buying Behaviour. International journal of business insights & transformation, 7(1).
• Park, C.‐H., & Kim, Y.‐G. (2003). Identifying key factors affecting consumer purchase behavior in an online shopping context. International Journal of Retail & Distribution Management.
• Permatasari, A., & Kartikowati, M. (2018). The influence of website design on customer online trust and perceived risk towards purchase intention: a case of O2O commerce in Indonesia. International Journal of Business and Globalisation, 21(1), 74-86.
• Roby, T., Ashe, S., Singh, N., & Clark, C. (2013). Shaping the online experience: How administrators can influence student and instructor perceptions through policy and practice. The Internet and Higher Education, 17, 29-37.
• Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training.
• Reed, S., Zolna, K., Parisotto, E., Colmenarejo, S. G., Novikov, A., Barth-Maron, G., ... & de Freitas, N. (2022). A generalist agent. arXiv preprint arXiv:2205.06175.
• Rudansky-Kloppers, S. (2014). Investigating factors influencing customer online buying satisfaction in Gauteng, South Africa. International Business & Economics Research Journal (IBER), 13(5), 1187–1198.
• Rudolph, J., Tan, S., & Tan, S. (2023). War of the chatbots: Bard, Bing Chat, ChatGPT, Ernie and beyond. The new AI gold rush and its impact on higher education. Journal of Applied Learning and Teaching, 6(1).
• Shanmugam, M., Wang, Y. Y., Bugshan, H., & Hajli, N. (2015). Understanding
customer perceptions of internet banking: the case of the UK. Journal of Enterprise Information Management, 28(5), 622-636.
• Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128, 13-35.
• Sakarya, S., & Soyer, N. (2014). Cultural differences in online shopping behavior: Turkey and the United Kingdom. “ International Journal of Electronic Commerce Studies”, 4(2), 213–238.
• Shim, S., & Drake, M. F. (1990). Consumer intention to utilize electronic shopping. The Fishbein behavioral intention model. Journal of direct marketing, 4(3), 22-33.
• Thorp, H. H. (2023). ChatGPT is fun, but not an author. Science, 379(6630), 313-313.
• Tong, X. (2010). A cross-national investigation of an extended technology acceptance model in the online shopping context. International Journal of Retail & Distribution Management, 38(10), 742-759.
• Uzun, H., & Poturak, M. (2014). Factors affecting online shopping behavior of consumers. European Journal of Social and Human Sciences. (3), 163–170.
• Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 425-478.
• Xiao, B., & Benbasat, I. (2007). E-commerceproduct recommendation agents: use, characteristics, and impact. MIS Quarterly, 31(1), 137-209.
• Yu, H. (2023). Reflection on whether Chat GPT should be banned by academia from the perspective of education and
P. Ramesh Bari & et al.
20
teaching. Frontiers in Psychology, 14, 1181712.
• Zhang, P., Aikman, S. N., & Sun, H. (2008). Two types of attitudes in ICT acceptance and use. Intl. Journal of Human-Computer Interaction, 24(7), 628-648.
